{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ccf0a2c5-6ed1-4b4d-b843-45baef63042c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "osm2gmns, 0.7.3\n"
     ]
    }
   ],
   "source": [
    "import os,sys\n",
    "#dataspath = '/home/qhs/Program/morphGPT/morphGPT/datas/'\n",
    "ROOTDIR = '/root/morphGPT/'\n",
    "dataspath = ROOTDIR + 'datas/'\n",
    "checkpoint_path = ROOTDIR + 'codes/checkpoints/'\n",
    "#\n",
    "os.chdir(ROOTDIR)\n",
    "\n",
    "from tqdm.notebook import trange, tqdm\n",
    "#\n",
    "import codes.DataProcess as DP\n",
    "import codes.model.ContrasiveRoadNetworkTripChainPretraining as model\n",
    "#\n",
    "import builtins\n",
    "#\n",
    "import pickle\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0b047874-dc05-4208-91e5-a5a52f6430d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device('cuda')\n",
    "reload(DP);reload(model)\n",
    "#\n",
    "system_configs = {'dim_embedding_map':8, 'mode_embedding_dim':10, 'num_mode_classification':8,}\n",
    "#\n",
    "#model = tripchaingenerative.to(device)\n",
    "tripchaingenerative = model.GenerativeTripChain_v2(mode_embedding_dim = system_configs['mode_embedding_dim'], \\\n",
    "                                morph_embeding_dim  = system_configs['dim_embedding_map'], \\\n",
    "                                mode_classsification_N = system_configs['num_mode_classification'], decoder_arg_nhead = 13)\n",
    "tripchaingenerative = tripchaingenerative.to(device)\n",
    "checkpoint = torch.load(checkpoint_path + 'model_v2.ckpt')\n",
    "tripchaingenerative.load_state_dict(checkpoint)\n",
    "#--------------------------------Inference, t_MAX_sec is max duration of trip chain\n",
    "GENERATED_TRIP = model.TripGeneration.ContinuousGeneration_using_GenerativeTripChain_v2(model = tripchaingenerative, t_MAX_sec = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "eedbdbfd-d21c-4f99-b182-a1c48d0d3eae",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[tensor([0., 0., 0., 5.]),\n",
       " tensor([ 0.0000,  0.0732, -0.0961,  4.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 0.0937,  0.1255, -0.2101,  4.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 0.1280,  0.1292, -0.2293,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 0.1603,  0.1885, -0.3172,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 0.2446,  0.1991, -0.3769,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 0.3263,  0.2077, -0.4375,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 0.4043,  0.2284, -0.4985,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 0.4569,  0.2364, -0.6169,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 0.5209,  0.2577, -0.6812,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 0.5209,  0.2487, -0.7470,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 0.5412,  0.2995, -0.8563,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 0.6047,  0.3160, -0.9402,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 0.6437,  0.3536, -1.0386,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 0.7154,  0.4031, -1.0784,  4.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 0.7635,  0.4207, -1.1508,  4.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 0.8663,  0.4535, -1.2050,  4.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 0.9442,  0.4756, -1.2607,  4.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 0.9780,  0.5034, -1.3450,  4.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 1.0535,  0.5438, -1.4508,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 1.1135,  0.5268, -1.5228,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 1.2138,  0.5537, -1.6188,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 1.2689,  0.6019, -1.7201,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 1.3456,  0.6339, -1.7823,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 1.3596,  0.6284, -1.8811,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 1.4118,  0.6319, -1.9521,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 1.4870,  0.6545, -2.0343,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 1.5079,  0.6789, -2.0995,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 1.5728,  0.7042, -2.2045,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 1.6942,  0.7325, -2.3078,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 1.7269,  0.7602, -2.3376,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 1.7324,  0.7833, -2.4153,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 1.7826,  0.7830, -2.5184,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 1.8548,  0.8362, -2.6184,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 1.9301,  0.8670, -2.7238,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 1.9494,  0.8688, -2.8037,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 2.0483,  0.9018, -2.8932,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 2.0898,  0.8986, -2.9895,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 2.1886,  0.9274, -3.0941,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 2.2359,  0.9540, -3.1969,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 2.3313,  0.9876, -3.3121,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 2.3589,  0.9991, -3.4020,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 2.4241,  1.0456, -3.5032,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 2.5299,  1.0705, -3.6107,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 2.6345,  1.1011, -3.7124,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 2.6345,  1.0834, -3.7470,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 2.7232,  1.0890, -3.8528,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 2.7709,  1.1073, -3.9383,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 2.8474,  1.1337, -4.0447,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 2.9090,  1.1505, -4.1640,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 2.9981,  1.1804, -4.2591,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 3.1291,  1.2198, -4.3447,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>)]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Each element is (t,lon,lat,mode)\n",
    "GENERATED_TRIP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "2bb41097-9e82-41cd-af95-b674344ac741",
   "metadata": {},
   "outputs": [],
   "source": [
    "from codes.draw import *\n",
    "from codes.ui_gif import *\n",
    "import imageio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "ffcfdab5-3595-4b4e-92ae-c29872ffd326",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "module = trip_data(GENERATED_TRIP)\n",
    "draw_data = module.gen_df()\n",
    "max_lim = draw_data.max(axis=0).iloc[1:3].to_numpy().astype(float) + 0.03\n",
    "min_lim = draw_data.min(axis=0).iloc[1:3].to_numpy().astype(float) - 0.03"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "18f28fbb-6980-4639-903c-c882d3e11ba4",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Co+vNLwQWJunLzMyKz58YNjNLMYeAmVmKOQTMzFLMIWBmlmIOATOzFHMImJmlmEPAzCzFHAJmZinmEDAzSzGHgJlZijkEzMxSzCFgZpZiDgEzsxRzCJiZpZhDwMwsxRwCZmYp5hAwM0uxRE8Wk3Qf8DfAQWAL8PcR8ac87bYC7wGHgUMRkUnSr5mZFUfSPYElwKCIuBR4GZjSSNvhEVHuADAzazsShUBEPJ17kDzACqAseUlmZtZainlOYDywqIF1ATwtaZWkiY1tRNJESdWSqmtqaopYnpmZfVCT5wQkLQV65Fk1NSLm5dpMBQ4BsxrYzJURsUPSucASSZsiYlm+hhExA5gBkMlkooAxmJnZcWoyBCJiRGPrJY0DrgOujoi8b9oRsSP39W1Jc4FhQN4QMDOz1pPocJCkUcA3gOsjYm8DbTpJ6lz7GhgJrE/Sr5mZFUfScwIPAZ3JHuJZI+mHAJJ6SlqYa9Md+LWktcBKYEFEPJWwXzMzK4JEnxOIiL9qYPmbwOjc61eBwUn6MTOzluFPDJuZpZhDwMwsxRwCZmYp5hAwM0sxh4CZWYo5BMzMUswhYGaWYg4BM7MUcwiYmaWYQ8DMLMUcAmZmKeYQMDNLMYeAmVmKOQTMzFLMIWBmlmIOATOzFEscApLulvRi7sliT0vq2UC7sZJ+n5vGJu3XzMySK8aewH0RcWlElAP/CXzrgw0knQ3cCXyU7EPm75TUpQh9m5lZAolDICL21JvtBESeZtcASyLinYj4I7AEGJW0bzMzSybRM4ZrSfoucAvwLjA8T5PzgW315rfnluXb1kRgIsAFF1xQjPLMzKwBBe0JSFoqaX2eqQogIqZGRC9gFjApSUERMSMiMhGROeecc5JsyszMmlDQnkBEjChwe7OAhWSP/9e3A/hEvfky4PkCt2lmZi2kGFcHXVRvtgrYlKfZYmCkpC65E8Ijc8vMzKyEinFO4F5J/YAjwOvAbQCSMsBtETEhIt6RdDfwQu577oqId4rQt5mZJaCIfBfztA2ZTCaqq6tLXYaZ2QlD0qqIyBTa3p8YNjNLMYeAmVmKOQTMzFLMIWBmlmIOATOzFHMImJmlmEPAzCzFHAJmZinmEDAzSzGHgJlZijkEzMxSzCFgZpZiDgEzsxRzCJiZpZhDwMwsxRwCZmYp5hAwM0uxRI+XzD0ysorsoyXfBsZFxJt52h0G1uVm34iI65P0a2ZmxZF0T+C+iLg0IsqB/wS+1UC7fRFRnpscAGZmbUSiEIiIPfVmOwFt94HFZmZ2jMTnBCR9V9I24PM0vCfQUVK1pBWSPtPE9ibm2lbX1NQkLc/MzBqhiMb/eJe0FOiRZ9XUiJhXr90UoGNE3JlnG+dHxA5JFwLPAldHxJamistkMlFdXd1UMzMzy5G0KiIyhbZv8sRwRIwocFuzgIXAMSEQETtyX1+V9DxQATQZAmZm1rISHQ6SdFG92SpgU542XSSdlnvdDbgC2JCkXzMzK45El4gC90rqR/YS0deB2wAkZYDbImIC0B94RNIRsqFzb0Q4BMzM2oBEIRARf9fA8mpgQu71cuCSJP2YmVnL8CeGzcxSzCFgZpZiDgEzsxRzCJiZpZhDwMwsxRwCZmYp5hAwM0sxh4CZWYo5BMzMUswhYGaWYg4BM7MUcwiYmaWYQ8DMLMUcAmZmKeYQMDNLMYeAmVmKOQTMzFKsaCEg6XZJkXuOcL71YyX9PjeNLVa/ZmZ2/JI+YxgASb2AkcAbDaw/G7gTyAABrJI0PyL+WIz+zczs+BRrT2A68A2yb/D5XAMsiYh3cm/8S4BRRerbzMyOU+IQkFQF7IiItY00Ox/YVm9+e25Zvu1NlFQtqbqmpiZpeWZm1oiCDgdJWgr0yLNqKvBNsoeCiiIiZgAzADKZTEN7FmZmVgQFhUBEjMi3XNIlQB9grSSAMmC1pGERsbNe0x3AJ+rNlwHPH0e9ZmZWRIkOB0XEuog4NyJ6R0Rvsod5hnwgAAAWAyMldZHUheyew+IkfZuZWXIt9jkBSRlJMwEi4h3gbuCF3HRXbpmZmZVQUS4RrZXbG6h9XQ1MqDf/I+BHxezPzMyS8SeGzcxSzCFgZpZiDgEzsxRzCJiZpZhDwMwsxRwCZmYp5hAwM0sxh4CZWYo5BMzMUkwRbfdGnZJqgNdLXUczdQN2lbqIFuYxnjzSMM60jfEjEXFOod/YpkPgRCSpOiIypa6jJXmMJ480jNNjbJwPB5mZpZhDwMwsxRwCxTej1AW0Ao/x5JGGcXqMjfA5ATOzFPOegJlZijkEzMxSzCFwHCSNkrRZ0iuSJudZ/zVJGyS9KOkZSR8pRZ1JNTXOeu3+TlJIOuEuwytkjJI+l/v3fEnST1u7xqQK+H29QNJzkn6X+50dXYo6k5D0I0lvS1rfwHpJejD3M3hR0pDWrrEYChjn53PjWydpuaTBTW40Ijw1YwLaA1uAC4FTgbXAgA+0GQ6ckXv9ZeDnpa67JcaZa9cZWAasADKlrrsF/i0vAn4HdMnNn1vqultgjDOAL+deDwC2lrru4xjnVcAQYH0D60cDiwABlwG/LXXNLTTOy+v9rl5byDi9J9B8w4BXIuLViDgIzAaq6jeIiOciYm9udgVQ1so1FkOT48y5G/jfwP7WLK5IChnjrcDDEfFHgIh4u5VrTKqQMQbwodzrs4A3W7G+ooiIZcA7jTSpAp6IrBXAhyWd1zrVFU9T44yI5bW/qxT43uMQaL7zgW315rfnljXkH8j+BXKiaXKcuV3qXhGxoDULK6JC/i3/GvhrSf8taYWkUa1WXXEUMsZvA1+QtB1YCPyP1imtVTX3/+3JoKD3nlNaoZDUkvQFIAN8vNS1FJukdsC/AeNKXEpLO4XsIaFPkP2rapmkSyLiT6UsqsjGAD+OiPslVQL/LmlQRBwpdWF2fCQNJxsCVzbV1nsCzbcD6FVvviy37CiSRgBTgesj4kAr1VZMTY2zMzAIeF7SVrLHWeefYCeHC/m33A7Mj4i/RMRrwMtkQ+FEUcgY/wH4BUBE/AboSPaGZCeTgv7fngwkXQrMBKoiYndT7R0CzfcCcJGkPpJOBW4G5tdvIKkCeIRsAJxox5BrNTrOiHg3IrpFRO+I6E32+OP1EVFdmnKPS5P/lsCvyO4FIKkb2cNDr7ZijUkVMsY3gKsBJPUnGwI1rVply5sP3JK7Sugy4N2IeKvURRWbpAuAJ4EvRsTLhXyPDwc1U0QckjQJWEz2yosfRcRLku4CqiNiPnAfcCYwRxLAGxFxfcmKPg4FjvOEVuAYFwMjJW0ADgN3FPLXVVtR4BhvBx6V9D/JniQeF7nLS04Ukn5GNqy75c5t3Al0AIiIH5I91zEaeAXYC/x9aSpNpoBxfgvoCvyf3HvPoWji7qK+bYSZWYr5cJCZWYo5BMzMUswhYGaWYg4BM7MUcwiYmaWYQ8DMLMUcAmZmKfb/AUJ0C2xord9QAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "images = []\n",
    "plt.ioff()\n",
    "filename='/root/morphGPT/dongtai.gif'\n",
    "for i in np.linspace(0, draw_data.shape[0] - 2, 10).astype(int) + 1:\n",
    "    temp_data = draw_data.iloc[:i, :]\n",
    "    plot_module = plot_travel_chain_no_osm(temp_data)\n",
    "\n",
    "    fig, ax = plot_module.plot_travel_chain_no_osm(max_lim, min_lim)\n",
    "\n",
    "    buf = BytesIO()\n",
    "    fig.savefig(buf, format='png')\n",
    "    buf.seek(0)\n",
    "    images.append(imageio.imread(buf))\n",
    "imageio.mimsave(filename, images, duration=100, loop=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "8cefe526-100e-43eb-a47d-1fa5b809ac7f",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "ename": "TclError",
     "evalue": "no display name and no $DISPLAY environment variable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTclError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_523/425529444.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     16\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mgif\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m     \u001b[0mroot\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtkinter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTk\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     19\u001b[0m     \u001b[0mroot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgeometry\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'880x460'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m     \u001b[0mroot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'动态出行链生成'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/tkinter/__init__.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, screenName, baseName, className, useTk, sync, use)\u001b[0m\n\u001b[1;32m   2021\u001b[0m                 \u001b[0mbaseName\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbaseName\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mext\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2022\u001b[0m         \u001b[0minteractive\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2023\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_tkinter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscreenName\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbaseName\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclassName\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minteractive\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwantobjects\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0museTk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msync\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2024\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0museTk\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2025\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_loadtk\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTclError\u001b[0m: no display name and no $DISPLAY environment variable"
     ]
    }
   ],
   "source": [
    "if __name__ == \"__main__\":\n",
    "    import tkinter\n",
    "\n",
    "\n",
    "    def delete():  # 删除临时图\n",
    "        root.destroy()\n",
    "        gif.close()\n",
    "\n",
    "    def show(file_name,label,root):\n",
    "        gif = playGif(file_name)\n",
    "        gif.playGif(root, label)  # 实现动态插放\n",
    "\n",
    "\n",
    "    def gen_g(file_name):\n",
    "        gif = playGif(file_name)\n",
    "        return gif\n",
    "\n",
    "    root = tkinter.Tk()\n",
    "    root.geometry('880x460')\n",
    "    root.title('动态出行链生成')\n",
    "    label = tkinter.Label(root,text=\"动图展示区域\",)\n",
    "    label.pack(side=tkinter.RIGHT)\n",
    "    root.protocol('WM_DELETE_WINDOW', delete)  #####【重要】关闭窗口后的事件：delete\n",
    "\n",
    "    button0 = tkinter.Button(root, text=\"generate\", command=lambda: show(filename,label,root), font = 120)\n",
    "    button0.pack(side=tkinter.LEFT)\n",
    "\n",
    "    root.mainloop()"
   ]
  }
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